Autonomous navigation for UAVs managing motion and sensing uncertainty

We present a motion planner for the autonomous navigation of UAVs that manages motion and sensing uncertainty at planning time. By doing so, optimal paths in terms of probability of collision, traversal time and uncertainty are obtained. Moreover, our approach takes into account the real dimensions of the UAV in order to reliably estimate the probability of collision from the predicted uncertainty. The motion planner relies on a graduated fidelity state lattice and a novel multi-resolution heuristic which adapt to the obstacles in the map. This allows managing the uncertainty at planning time and yet obtaining solutions fast enough to control the UAV in real time. Experimental results show the reliability and the efficiency of our approach in different real environments and with different motion models. Finally, we also report planning results for the reconstruction of 3D scenarios, showing that with our approach the UAV can obtain a precise 3D model autonomouslyThis research was funded by the Spanish Ministry for Science, Innovation, Spain and Universities (grant TIN2017-84796-C2-1-R) and the Galician Ministry of Education, University and Professional Training, Spain (grants ED431C 2018/29 and “accreditation 2016–2019, ED431G/08”). These grants were co-funded by the European Regional Development Fund (ERDF/FEDER program)S